---
title: "Computational Musicology Dashboard 2023"
output:
flexdashboard::flex_dashboard:
storyboard: true
social: menu
source: embed
orientation: columns
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidymodels)
library(tibble)
library(ggdendro)
library(stringr)
library(purrr)
library(scales)
library(tidyr)
library(remotes)
library(spotifyr)
library(ggplot2)
library(dplyr)
library(compmus)
all_out_2010 <- get_playlist_audio_features("", "37i9dQZF1DX5Ejj0EkURtP")
all_out_2000 <- get_playlist_audio_features("", "37i9dQZF1DX4o1oenSJRJd")
```
Introduction
================
Column {.tabset}
--------------------------------
### Tempo Distribution differences
```{r}
ggplot(height=400, width=500) +
geom_density(data = all_out_2010, aes(x=tempo, fill="All Out 2010s"), alpha=0.5) +
geom_density(data = all_out_2000, aes(x=tempo, fill="All Out 2000s"), alpha=0.5) +
labs(title="Tempo Distribution", x="Tempo", y="Density") +
scale_fill_manual(values=c("blue", "green"))
```
### Energy vs Valence
```{r}
ggplot(height=600, width=5600) +
geom_point(data = all_out_2010, aes(x=energy, y=valence, color="All Out 2010s"), alpha=0.5) +
geom_point(data = all_out_2000, aes(x=energy, y=valence, color="All Out 2000s"), alpha=0.5) +
labs(title="Energy vs. Valence", x="Energy", y="Valence") +
scale_color_manual(values=c("blue", "green"))
```
Column {data-width= 400}
----------------------
### Information about the corpus and some graphs
The corpora I chose are playlists from Spotify. These are playlists I really often listen to when I am studying or travelling for example. It consists of a lot of different kind of music and everytime it has something I really enjoy listening to. The playlists are the Spotify playlist called "ALL OUT OF 2010s" and "ALL OUT OF 2000s". I think there is alot of different songs/artists/genres etc. There are a few artists who have released very big and many songs and appear more often in this list so that will also lead to interesting results. The artists are mainly Adele, Justin Bieber and Shawn Mendes
What I am eager about is, is to find new corelations or insights that you can not see or hear when you are listening to the playlist. The 2010s saw the emergence of new genres, such as trap and EDM, as well as the continued popularity of established genres like pop, rock, and hip-hop. This diversity of musical styles offers a rich and varied corpus for analysis and exploration.
Additionally, the 2010s marked a time of significant social and cultural change, with movements like Black Lives Matter and Me Too having a major impact on the music industry and the messages conveyed through music. This could make the corpus of music from the 2010s particularly interesting for researchers interested in studying the intersection of music and social movements.
Chromagrams
============================================
Column {.tabset}
--------------------------------
### Mirrors by Justin Timberlake
```{r}
mirrors <-
get_tidy_audio_analysis("4rHZZAmHpZrA3iH5zx8frV") |>
select(segments) |>
unnest(segments) |>
select(start, duration, pitches)
mirrors |>
mutate(pitches = map(pitches, compmus_normalise, "euclidean")) |>
compmus_gather_chroma() |>
ggplot(
aes(
x = start + duration / 2,
width = duration,
y = pitch_class,
fill = value
)
) +
geom_tile() +
labs(x = "Time (s)", y = NULL, fill = "Magnitude") +
theme_minimal() +
scale_fill_viridis_c()
```
### Stan by Eminem
```{r}
stan <-
get_tidy_audio_analysis("3UmaczJpikHgJFyBTAJVoz") |>
select(segments) |>
unnest(segments) |>
select(start, duration, pitches)
stan |>
mutate(pitches = map(pitches, compmus_normalise, "euclidean")) |>
compmus_gather_chroma() |>
ggplot(
aes(
x = start + duration / 2,
width = duration,
y = pitch_class,
fill = value
)
) +
geom_tile() +
labs(x = "Time (s)", y = NULL, fill = "Magnitude") +
theme_minimal() +
scale_fill_viridis_c()
```
Column {data-width= 400}
----------------------
### Text about the chromagrams
text about the imagess
Dit is allemaal wat bullshit om ff te kijken hoe dit er uit zie t watn ik heb geen idee lallamlaflasdfioasfjiosdjfio
Ceptograms
============================================
Column {.tabset}
--------------------------------
### Mirrors by Justin Timberlake
```{r}
mirrors <-
get_tidy_audio_analysis("4rHZZAmHpZrA3iH5zx8frV") |> # Change URI.
compmus_align(bars, segments) |> # Change `bars`
select(bars) |> # in all three
unnest(bars) |> # of these lines.
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "rms", norm = "euclidean" # Change summary & norm.
)
) |>
mutate(
timbre =
map(segments,
compmus_summarise, timbre,
method = "rms", norm = "euclidean" # Change summary & norm.
)
)
mirrors |>
compmus_gather_timbre() |>
ggplot(
aes(
x = start + duration / 2,
width = duration,
y = basis,
fill = value
)
) +
geom_tile() +
labs(x = "Time (s)", y = NULL, fill = "Magnitude") +
scale_fill_viridis_c() +
theme_classic()
```
### Stan by Eminem
```{r}
stan <-
get_tidy_audio_analysis("3UmaczJpikHgJFyBTAJVoz") |> # Change URI.
compmus_align(bars, segments) |> # Change `bars`
select(bars) |> # in all three
unnest(bars) |> # of these lines.
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "rms", norm = "euclidean" # Change summary & norm.
)
) |>
mutate(
timbre =
map(segments,
compmus_summarise, timbre,
method = "rms", norm = "euclidean" # Change summary & norm.
)
)
stan |>
compmus_gather_timbre() |>
ggplot(
aes(
x = start + duration / 2,
width = duration,
y = basis,
fill = value
)
) +
geom_tile() +
labs(x = "Time (s)", y = NULL, fill = "Magnitude") +
scale_fill_viridis_c() +
theme_classic()
```
Column {data-width= 400}
----------------------
### Text about the ceptograms
text about the imagess
Dit is allemaal wat bullshit om ff te kijken hoe dit er uit zie t watn ik heb geen idee lallamlaflasdfioasfjiosdjfio
Self-Similarity Matrices
==============================
Column {.tabset}
--------------------------------
### Mirrors by Justin Timberlake
```{r}
mirrors <-
get_tidy_audio_analysis("4rHZZAmHpZrA3iH5zx8frV") |> # Change URI.
compmus_align(bars, segments) |> # Change `bars`
select(bars) |> # in all three
unnest(bars) |> # of these lines.
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "rms", norm = "euclidean" # Change summary & norm.
)
) |>
mutate(
timbre =
map(segments,
compmus_summarise, timbre,
method = "rms", norm = "euclidean" # Change summary & norm.
)
)
mirrors |>
compmus_self_similarity(timbre, "cosine") |>
ggplot(
aes(
x = xstart + xduration / 2,
width = xduration,
y = ystart + yduration / 2,
height = yduration,
fill = d
)
) +
geom_tile() +
coord_fixed() +
scale_fill_viridis_c(guide = "none") +
theme_classic() +
labs(x = "", y = "")
```
### Stan by Eminem
```{r}
stan <-
get_tidy_audio_analysis("3UmaczJpikHgJFyBTAJVoz") |> # Change URI.
compmus_align(bars, segments) |> # Change `bars`
select(bars) |> # in all three
unnest(bars) |> # of these lines.
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "rms", norm = "euclidean" # Change summary & norm.
)
) |>
mutate(
timbre =
map(segments,
compmus_summarise, timbre,
method = "rms", norm = "euclidean" # Change summary & norm.
)
)
stan |>
compmus_self_similarity(timbre, "cosine") |>
ggplot(
aes(
x = xstart + xduration / 2,
width = xduration,
y = ystart + yduration / 2,
height = yduration,
fill = d
)
) +
geom_tile() +
coord_fixed() +
scale_fill_viridis_c(guide = "none") +
theme_classic() +
labs(x = "", y = "")
```
Column {data-width= 400}
----------------------
### Text about the SSM
text about the imagess
Dit is allemaal wat bullshit om ff te kijken hoe dit er uit zie t watn ik heb geen idee lallamlaflasdfioasfjiosdjfio
Chordograms
=====================================
Column {.tabset}
--------------------------------
```{r}
circshift <- function(v, n) {
if (n == 0) v else c(tail(v, n), head(v, -n))
}
# C C# D Eb E F F# G Ab A Bb B
major_chord <-
c( 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0)
minor_chord <-
c( 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0)
seventh_chord <-
c( 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0)
major_key <-
c(6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88)
minor_key <-
c(6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17)
chord_templates <-
tribble(
~name, ~template,
"Gb:7", circshift(seventh_chord, 6),
"Gb:maj", circshift(major_chord, 6),
"Bb:min", circshift(minor_chord, 10),
"Db:maj", circshift(major_chord, 1),
"F:min", circshift(minor_chord, 5),
"Ab:7", circshift(seventh_chord, 8),
"Ab:maj", circshift(major_chord, 8),
"C:min", circshift(minor_chord, 0),
"Eb:7", circshift(seventh_chord, 3),
"Eb:maj", circshift(major_chord, 3),
"G:min", circshift(minor_chord, 7),
"Bb:7", circshift(seventh_chord, 10),
"Bb:maj", circshift(major_chord, 10),
"D:min", circshift(minor_chord, 2),
"F:7", circshift(seventh_chord, 5),
"F:maj", circshift(major_chord, 5),
"A:min", circshift(minor_chord, 9),
"C:7", circshift(seventh_chord, 0),
"C:maj", circshift(major_chord, 0),
"E:min", circshift(minor_chord, 4),
"G:7", circshift(seventh_chord, 7),
"G:maj", circshift(major_chord, 7),
"B:min", circshift(minor_chord, 11),
"D:7", circshift(seventh_chord, 2),
"D:maj", circshift(major_chord, 2),
"F#:min", circshift(minor_chord, 6),
"A:7", circshift(seventh_chord, 9),
"A:maj", circshift(major_chord, 9),
"C#:min", circshift(minor_chord, 1),
"E:7", circshift(seventh_chord, 4),
"E:maj", circshift(major_chord, 4),
"G#:min", circshift(minor_chord, 8),
"B:7", circshift(seventh_chord, 11),
"B:maj", circshift(major_chord, 11),
"D#:min", circshift(minor_chord, 3)
)
key_templates <-
tribble(
~name, ~template,
"Gb:maj", circshift(major_key, 6),
"Bb:min", circshift(minor_key, 10),
"Db:maj", circshift(major_key, 1),
"F:min", circshift(minor_key, 5),
"Ab:maj", circshift(major_key, 8),
"C:min", circshift(minor_key, 0),
"Eb:maj", circshift(major_key, 3),
"G:min", circshift(minor_key, 7),
"Bb:maj", circshift(major_key, 10),
"D:min", circshift(minor_key, 2),
"F:maj", circshift(major_key, 5),
"A:min", circshift(minor_key, 9),
"C:maj", circshift(major_key, 0),
"E:min", circshift(minor_key, 4),
"G:maj", circshift(major_key, 7),
"B:min", circshift(minor_key, 11),
"D:maj", circshift(major_key, 2),
"F#:min", circshift(minor_key, 6),
"A:maj", circshift(major_key, 9),
"C#:min", circshift(minor_key, 1),
"E:maj", circshift(major_key, 4),
"G#:min", circshift(minor_key, 8),
"B:maj", circshift(major_key, 11),
"D#:min", circshift(minor_key, 3)
)
```
### Mirrors by Justin Timberlake
```{r}
mirrors <-
get_tidy_audio_analysis("4rHZZAmHpZrA3iH5zx8frV") |>
compmus_align(sections, segments) |>
select(sections) |>
unnest(sections) |>
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "mean", norm = "manhattan"
)
)
mirrors |>
compmus_match_pitch_template(
key_templates, # Change to chord_templates if descired
method = "euclidean", # Try different distance metrics
norm = "manhattan" # Try different norms
) |>
ggplot(
aes(x = start + duration / 2, width = duration, y = name, fill = d)
) +
geom_tile() +
scale_fill_viridis_c(guide = "none") +
theme_minimal() +
labs(x = "Time (s)", y = "")
```
### Stan by Eminem
```{r}
stan <-
get_tidy_audio_analysis("3UmaczJpikHgJFyBTAJVoz") |>
compmus_align(sections, segments) |>
select(sections) |>
unnest(sections) |>
mutate(
pitches =
map(segments,
compmus_summarise, pitches,
method = "mean", norm = "manhattan"
)
)
stan |>
compmus_match_pitch_template(
key_templates, # Change to chord_templates if descired
method = "euclidean", # Try different distance metrics
norm = "manhattan" # Try different norms
) |>
ggplot(
aes(x = start + duration / 2, width = duration, y = name, fill = d)
) +
geom_tile() +
scale_fill_viridis_c(guide = "none") +
theme_minimal() +
labs(x = "Time (s)", y = "")
```
Column {data-width= 400}
----------------------
### Text about the chordograms
text about the imagess
Dit is allemaal wat bullshit om ff te kijken hoe dit er uit zie t watn ik heb geen idee lallamlaflasdfioasfjiosdjfio
Tempograms/Dendrograms
==================================
Column {.tabset}
--------------------------------
### Dendrogram clustering
```{r}
out2010 <-
get_playlist_audio_features("", "37i9dQZF1DX5Ejj0EkURtP") |>
add_audio_analysis() |>
mutate(
segments = map2(segments, key, compmus_c_transpose),
pitches =
map(segments,
compmus_summarise, pitches,
method = "mean", norm = "manhattan"
),
timbre =
map(
segments,
compmus_summarise, timbre,
method = "mean"
)
) |>
mutate(pitches = map(pitches, compmus_normalise, "clr")) |>
mutate_at(vars(pitches, timbre), map, bind_rows) |>
unnest(cols = c(pitches, timbre))
out2010_juice <-
recipe(
track.name ~
danceability +
energy +
loudness +
speechiness +
acousticness +
instrumentalness +
liveness +
valence +
tempo +
duration +
C + `C#|Db` + D + `D#|Eb` +
E + `F` + `F#|Gb` + G +
`G#|Ab` + A + `A#|Bb` + B +
c01 + c02 + c03 + c04 + c05 + c06 +
c07 + c08 + c09 + c10 + c11 + c12,
data = out2010
) |>
step_center(all_predictors()) |>
step_scale(all_predictors()) |>
prep(out2010 |> mutate(track.name = str_trunc(track.name, 20))) |>
juice() |>
column_to_rownames("track.name")
out2010_dist <- dist(out2010_juice, method = "euclidean")
out2010_dist |>
hclust(method = "complete") |>
dendro_data() |>
ggdendrogram()
```
### Mirrors by Justin Timberlake
```{r}
#mirrors <- get_tidy_audio_analysis("4rHZZAmHpZrA3iH5zx8frV")
#
#mirrors |>
# tempogram(window_size = 8, hop_size = 1, cyclic = TRUE) |>
# ggplot(aes(x = time, y = bpm, fill = power)) +
# geom_raster() +
# scale_fill_viridis_c(guide = "none") +
# labs(x = "Time (s)", y = "Tempo (BPM)") +
# theme_classic()
```
### Stan by Eminem
```{r}
#stan <- get_tidy_audio_analysis("3UmaczJpikHgJFyBTAJVoz")
#
#stan |>
# tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) |>
# ggplot(aes(x = time, y = bpm, fill = power)) +
# geom_raster() +
# scale_fill_viridis_c(guide = "none") +
# labs(x = "Time (s)", y = "Tempo (BPM)") +
# theme_classic()
```
Column {data-width= 400}
----------------------
### Text about the dendrogram and tempograms
text about the imagess
Dit is allemaal wat bullshit om ff te kijken hoe dit er uit zie t watn ik heb geen idee lallamlaflasdfioasfjiosdjfio
Conclusion and Thoughts
==================================
The first thing to conclude is that it is not easy with these corpora to see very big differences.
